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Jamie Doiron

EDTECH 505

Chapter 6

1. What are the differences between qualitative and quantitative?

The qualitative technique for data collection requires up-close observation and description of

what is happening. The data being collected is usually about what is happening at the present

moment and does not include any information regarding results of the actions occurring. The

focus is on developing meaning by observing actions rather than through gathering raw

numerical data about an event.

The quantitative technique for data collection, on the other hand, is all about concrete numerical

analysis. Data can be collected through tests, counts, measures and other instruments.

Qualitative data is used to make predictions, determine results, and show causal relationships

between events.

In my evaluation project, I am trying to determine if Google Sites is an adequate tool to use as an

LMS for my school district based on their needs. I could gather qualitative data about staff needs

and desires through surveys. Another source of qualitative data would be interviews with

administrators about their goals in adopting some type of LMS. I could gather quantitative data for

my evaluation from web analytics that show usage of Google Sites over time or by quizzing staff

members on how to use Google Sites to determine their comfort level with the software.

2. What are the levels of data you might encounter?

Nominal data relates to only one principal. This type of data could be either qualitative or

quantitative, but each associated data entry should be unique. For example, if staff members

were filling out a survey for my evaluation project and they had to answer the following question:

How many months have you used Google Sites? Less than 3, 4-6, 7-12, more than 12, the

data would be nominal because it is all about one topic and each responder can only choose one

answer.

Ordinal data is also based on one principle, but it is used to convey order or rank. An example of

this type of data is from survey questions where the respondents have to rank something as

high, medium, low or agree, neutral, or disagree. If you were to change the order of the items, it

would no longer make sense. Additionally, ordinal data does not imply equal distance between

each category. An example of how I might encounter ordinal data on my evaluation project is if I

asked survey respondents to rank five pieces of software according to their comfort level, where

each piece had to receive a different number between 1 and 5.

Interval data also implies a rank or order of some kind, but it involves equal intervals. That is to

say that the difference between 3 and 4 is the same as between 33 and 34. This type of data is

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often used on tests. It is likely that I would encounter this type of data if I were to quiz staff

members on Google Sites to determine what their pre-existing knowledge of the software was.

The last type of data is ratio data. It has all of the same characteristics of nominal, ordinal and

interval data but it also has an absolute zero point. This means that negative numbers and

positive numbers have the same distance between them, so -10 to 10 has a distance of 20,which is the same as 20-40. This is the type of data I would encounter if I researched web traffic

analytics on google sites to see how often faculty and staff were using it.

3. What are some instruments that you might use or develop?

My evaluation project will rely heavily on survey and interview data to determine first what the

districts needs are from Google Sites. This data will largely be qualitative and likely both nominal

and ordinal. The best way for me to collect this data is through in-person observation and online

surveys that are developed by me. Other instruments that I would use to collect data are tests to

see what the users already know about Google Sites, since it is being used in district currently.

Also, I will likely perform outside research about the capabilities of Google Sites and compare

them to the needs of the district. This research data can be recorded in either a quantitative or

qualitative way, but I think the best thing to do is treat it quantitatively. I could use a spreadsheet

to compare the features of Google Sites against the district needs and put a 1 if they match or a

0 if they are not aligned. That will result in an overall score that can be manipulated. For example,

I can give weight to certain categories of needs if they are more important than others.